The CartPole game with Keras

CartPole is one of the simpler environments in the OpenAI Gym (a game simulator). The goal of CartPole is to balance a pole connected with one joint on top of a moving cart. Instead of pixel information, there are two kinds of information given by the state: the angle of the pole and position of the cart. An agent can move the cart by performing a sequence of actions of 0 or 1 to the cart, pushing it left or right:

The OpenAI Gym makes interacting with the game environment really simple:

next_state, reward, done, info = env.step(action)

In the preceding code, an action can be either 0 or 1. If we pass those numbers, env, which is the game environment, will emit the results. The done variable is a Boolean value saying whether the game ended or not. The old state information is paired with action, next_state, and reward is the information we need for training the agent.

How to do it...

We will be using a neural network to build the AI agent that plays Cartpole. The...